---
title: XEMP qualitative strength
description: Understand how the qualitative strength indicators for XEMP Prediction Explanations are calculated. 
---

# XEMP qualitative strength {: #xemp-qualitative-strength }

[XEMP-based Prediction Explanations](xemp-pe#interpret-xemp-prediction-explanations) provide a visual indicator of the qualitative strength of each explanation presented by the insight. In the API, these values are returned from the [`qualitativeStrength` response parameter](dep-predex#qualitativestrength-indicator)  of the Prediction Explanation API endpoint.

The distribution is approximated from the validation data; the preview is computed on the validation data. 

## Score translations {: score-translations }

The boundaries between indicators (for example, `+++`, `++`, and `+`) are different when there are different numbers of features in a model. The tables below describe, based on feature count, how the calculations translate to the visual representation.  

Some notes:

* If an explanation’s score is trivial and has little or no qualitative effect, the output displays three grayed out symbols (`+++` or `---`). This indicates, for the represented directionality, that the effect is minor. 

* When there are a large number of features, a normalized score greater than 0.2 is represented as `+++`, so it is possible for multiple features to display this symbolic score in a single row.
	
In the tables, `q` represents the "qualitative" (or "normalized") score. 


### Features = 1 {: #features-1 }

The following describes the displayed symbolic score based on the calculated qualitative score for models built with a single feature:

Qualitative Score | Symbolic Score
----------------- | --------------
q <= -0.001	| `---`
-0.001 < q <= 0	| grayed-out `---`
0 < q < 0.001	| grayed-out `+++`
q >= 0.001	| `+++`


### Features = 2 {: #features-2 }

The following describes the displayed symbolic score based on the calculated qualitative score for models built with two features:

Qualitative Score | Symbolic Score
----------------- | --------------
q < -0.75	| `---`
-0.75 <= q < -0.25	| `--`
-0.25 <= q <= -0.001	| `-`
-0.001 < q <= 0	 | grayed-out `---`
0 < q < 0.001	| grayed-out `+++`
0.001 <= q <= 0.25	| `+`
0.25 < q <= 0.75	| `++`
q > 0.75 | `+++`


### Features >= 2, < 10 {: #features-2-10 }

The following describes the displayed symbolic score based on the calculated qualitative score for models built with more than two but fewer than 10 features:

Qualitative Score | Symbolic Score
----------------- | --------------
q < -2 / num_features | `---`
-2 / num_features <= q < -1 / (2 * num_features) | `--`
-1 / (2 * num_features) <= q <= -0.001 | `-`
-0.001 < q <= 0 | grayed-out `---`
0 < q < 0.001 | grayed-out `+++`
0.001 <= q <= 1 / (2 * num_features) | `+`
1 / (2 * num_features) < q <= 2 / num_features | `++`
q > 2 / num_features | `+++`

### Features >= 10 {: #features-10 }

The following describes the displayed symbolic score based on the calculated qualitative score for models built with 10 or more features:

Qualitative Score | Symbolic Score
----------------- | --------------
q < -0.2 | `---`
-0.2 <= q < -0.05 | `--`
-0.05 <= q <= -0.001 | `-`
-0.001 < q <= 0 | grayed-out `---`
0 < q < 0.001 | grayed-out `+++`
0.001 <= q <= 0.05 | `+`
0.05 < q <= 0.2 | `++`
q > 0.2 | `+++`